Tech

LLMs in Late 2026: How the Models and My Development Setup Changed

October 31, 2026

The last few weeks have brought several model announcements that caught my attention. OpenAI released GPT-6.1 Terra, with capabilities close to Sonnet 5.5 at half the API price. Google followed Gemini 4 Argon, which competes with GPT-6 Astra and Claude Opus 5.5, with the more capable Gemini 4 Carbon about a month later. And Kimi K3.1 Pro arrived from China, claiming performance close to Opus 5.5 and Astra.

A lot has changed over the past few months, so I wanted to sort it out: where the latest releases stand, how we got here, and how my own coding setup and thinking changed along the way. Then I will look at how to live with tools that keep improving, including the systems I build around them. I seem to upgrade my gear every time I read an announcement. Time for an inventory check.

Where We Are: What the Latest Models Are Competing On

I picked three recent announcements to see what each emphasizes. API prices below are in US dollars per million tokens, listed as uncached input and output prices, in that order.

Gemini 4 Carbon: Google Competes at the Top End Again

The October 28 Carbon announcement covers eight areas: general intelligence, agentic coding, computer use, knowledge work, multimodal understanding, long-context understanding, science and mathematics, and cybersecurity. Each is measured through one or more benchmarks.

The main comparisons include Argon, announced about a month earlier, GPT-6 Astra, Claude Opus 5.5, and Claude Fable 5.1. Carbon has the highest score among the listed models on 15 of the 17 metrics in Google’s table. Astra leads on one coding metric, and Opus 5.5 leads on computer use. Still, the positioning is clear: a model competing at the top across a broad range of work.

After a long stretch focused on Flash updates, Argon and Carbon bring Google back into the race to build the most capable models.

The introductory price matches Argon at $2/$10, rising to $4/$20 afterward. Access initially goes to a limited group of cyber defenders. A general availability date has not been announced.

GPT-6.1 Terra: A Lower-Cost Alternative to Sonnet 5.5

The October 26 GPT-6.1 Terra announcement emphasizes cost and speed.

For everyday use, the comparison I care about is Sonnet 5.5. Terra costs $1/$5, exactly half Sonnet’s $2/$10. My reading is that Terra gives up some overall capability in exchange for a lower price.

In Artificial Analysis’s comparison, Terra scores about 51 on the Intelligence Index, versus about 54 for Sonnet. Both use maximum reasoning effort, with Sonnet measured in its Default Fallback configuration. On FrontierCode 2.0, which measures code changes in real repositories, they are nearly tied: 42.3% for Terra and 41.7% for Sonnet.

OpenAI also publishes comparisons where Sonnet leads in coding and computer use. On Agents’ Last Exam, though, it says Terra completes tasks in less than a third of the time Sol and Astra take, and at a lower task cost than Opus 5.5.

GPT-6 Sol and Luna had arrived, followed by 6.1 Sol, while Terra was still on 5.6. I had expected the update to beat Sonnet 5.5 at the same price, so I was a little disappointed. But it has strengths worth trying. For agentic coding, I want to compare it with Sonnet 5.5 myself. For computer use, including browsers, video editing, and CAD, I am curious to see what it can do.

Kimi K3.1 Pro: Competing with the Leaders in the US and China

The October 23 Kimi K3.1 Pro announcement compares it with Astra and Opus, as well as DeepSeek V4.1 Flash and GLM-5.3.

Kimi’s published results are close to those of Opus 5.5 and Astra on some coding and knowledge-work tasks, and exceed them on others. They also show leads over GLM-5.3 and DeepSeek V4.1 Flash on several metrics.

DeepSeek V4.1 Flash still offers unusually good value, though. Its September announcement showed it competing with GPT-5.6 Sol and Opus 5 on some benchmarks. Yet peak pricing is just $0.30/$1.20, with off-peak rates half that. It does not have to match the latest flagship models across the board to remain useful at that price.

I am starting to think that “a little less capable, but cheaper” is no longer enough to describe models from China.

The Past Few Months: More Capability at Everyday Prices

Until GPT-5.6 Sol appeared in late June, my impression was that Anthropic led on capability, OpenAI was chasing it, and Google competed on price and integration with its own services. Since then, everyone has kept improving their best models while bringing more of those capabilities to cheaper models. Here is how I see the changes by company.

Anthropic: Bringing High-End Capabilities to Everyday Work

From April’s Mythos Preview to June’s Fable and Mythos 5, Anthropic had my attention at the top end. But using Fable at $10/$50 for routine coding takes some courage. Then Sonnet 5 arrived in late June with Opus 4.8-level capability on some tasks at $2/$10. July’s Opus 5 brought capability close to Fable 5 at half the price, $5/$25.

September’s Fable and Mythos 5.1 raised the bar again, while Opus 5.5 brought Fable 5.1-level performance on many jobs to $4/$20. Sonnet 5.5 improved without changing its $2/$10 price, and October’s Haiku 5.5 strengthened the lower-cost option for high-volume work. With OpenAI and the Chinese labs closing in, Anthropic has been making more of its high-end capability practical for everyday use.

For development, Claude Code remains attractive because its harness is so capable. I have strong feelings about avoiding vendor lock-in. My principles, it turns out, have a convenience threshold.

OpenAI: Closing the Gap While Expanding the Price Range

The GPT-5.6 rollout began with a limited Sol preview in late June, followed by general availability of Sol, Terra, and Luna in July. It offered three tiers of capability and price. Sol approached Fable 5 and exceeded it on some work tasks. Further price cuts followed, and September’s GPT-6 Astra competed at the top end with Fable 5.1 and Opus 5.

Later that September, GPT-6 Sol and Luna arrived at $2/$10 and $0.10/$0.50 respectively. GPT-6.1 Sol followed with performance close to Astra at one-fifth the price per token. October’s GPT-6.1 Terra brought the price down to $1/$5, half that of Sonnet 5.5 or Sol.

I had generally thought of OpenAI as the cheaper way to access capable LLMs compared with Anthropic. Sonnet 5.5 has made that picture less clear over the past month.

Google: From Flash Updates to Competing at the Top End

From July through September, Gemini 3.6 through 3.8 Flash kept improving while maintaining an introductory price of $0.75/$3.75. On some coding benchmarks, 3.8 produced results close to Opus 5 and 5.6 Sol. Google has long emphasized multimodal capabilities, including images, video, and audio. My impression is that the gap there is narrowing, too. Still, if I had a batch of videos to analyze, I would probably consider 3.8 Flash.

Google compared late September’s Gemini 4 Argon with Astra and Opus 5.5, and October’s Gemini 4 Carbon pushed performance further. Both have introductory prices of $2/$10, rising to $4/$20 afterward. Alongside price and integration, Google is competing more visibly at the top end again.

The Chinese Labs: From Low-Cost Options to the Top End

April’s DeepSeek V4-Pro and Flash stood out for their open weights and low prices. Pro launched at $1.74/$3.48, then dropped to $0.435/$0.87 under a discount that became permanent in May. Given the performance, the price looked like a bug. It got plenty of attention. June’s GLM-5.2 also approached Opus 4.8 on some long-running coding tasks, adding another way to use capable models at a lower price.

July’s Kimi K3 claimed performance close to Fable 5 and 5.6 Sol. August brought Qwen3.8-Max and GLM-5.3. GLM-5.3-Flash added image understanding and claimed to beat GLM-5.2 at a regular price of $0.15/$0.50. September’s DeepSeek V4.1 Flash improved performance on coding and professional tasks at a peak price of just $0.30/$1.20.

Then October’s Kimi K3.1 Pro claimed performance close to the latest Astra and Opus 5.5, while dropping K3’s $3/$15 price to $2/$10. As I mentioned earlier, the idea that models from China trade some capability for a lower price no longer captures the whole picture.

Looking across these releases, I do not see everyone moving into a new phase together. It looks more like each company is closing gaps in areas where another company is strong, on both capability and price.

How My Development Setup Changed

That is the market view. Here is what I was doing myself.

Until around June, I used Claude Code and Codex, switching to Google’s Antigravity when I hit my usage limits. I also tried pairing cheap models with OpenClaw and Hermes Agent. But I still had security concerns, and ended up sticking with Claude Code and Codex for everyday development.

As cost became more of a concern, and models from China attracted attention, I looked into DeepSeek and GLM. I settled on two agent roles inside OpenCode: a Thinker using a higher-end OpenAI model, and an Implementer using DeepSeek or GLM. I would work with the Thinker to investigate the problem and flesh out the implementation approach, put that into a GitHub Issue, and have the Implementer build it.

Around this time, I also started thinking more seriously about my own approach to loop engineering. I keep my custom agent skills and subagent definitions in a dedicated repository. The loop covers planning, implementation, review, and fixes. I also use what went wrong to improve the instructions and workflows themselves.

I think this was my primary setup for about a month, changing models along the way. I tried GLM-5.2, later GLM-5.3-Flash, and Gemini Flash models as another option. There were frustrations: some cheaper models were inconsistent, some models I used could not accept images, and remote control from my phone was awkward. I was using herdr with Tailscale for that.

As the vendors released capable models at lower prices, the more polished harnesses and mobile apps brought me back to Claude Code and Codex. Their remote-control features let me control sessions running on my PC from my phone. The Thinker/Implementer split stayed the same.

As best I remember, I gradually shifted more of my work to Codex after GPT-5.6 came out, then used Claude Code more after Sonnet 5.5. Terra has now brought my Codex usage back up a little, and Google’s announcements have me thinking about trying Antigravity more, too.

I mostly use each company’s subscription at around $20 a month, with occasional top-ups. That has been enough for building a few simple web apps. I avoid relying on a single vendor because I want to avoid lock-in and keep trying different tools. At the same time, I want to get more out of the harness features, especially in Claude Code.

How Long Should I Keep the Systems I Build?

Looking back, I have kept reconsidering my development environment as model capabilities and prices changed. A workaround that once made sense might become unnecessary with the next model or harness feature. Maintaining everything I have built is not necessarily the best use of my time.

At first, I wanted to avoid depending on a vendor, so I focused on procedures I could use across OpenCode, Codex, Claude Code, and Antigravity. I tried not to make harness-specific features a requirement. But practicing loop engineering has also shown me how useful those features can be. Lately, I have relaxed that constraint a little.

I still want to choose the environment that works best at the time, taking model capability, price, harness features, and the app itself into account. I want the convenient features. I also want to keep the cost of switching manageable when something better comes along. I am still figuring out that balance.

The models seem to be converging. My subscriptions keep multiplying.

Comments (0)

No comments yet. Be the first!